Road-based travel recommendation using geo-tagged images
Geotagged photos on social media like Flickr explicitly indicate the trajectories of tourists. They can be employed to reveal the tourists’ preference on landmarks and routings of tourism. Most of existing works on routing searches are based on the trajectories of GPS-enabled devices’ users. From a...
Gespeichert in:
| Hauptverfasser: | , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
2015
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| In: |
Computers, environment and urban systems
Year: 2013, Jahrgang: 53, Pages: 110-122 |
| DOI: | 10.1016/j.compenvurbsys.2013.07.006 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1016/j.compenvurbsys.2013.07.006 Verlag, lizenzpflichtig, Volltext: http://www.sciencedirect.com/science/article/pii/S0198971513000677 
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| Verfasserangaben: | Yeran Sun, Hongchao Fan, Mohamed Bakillah, Alexander Zipf |
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| 520 | |a Geotagged photos on social media like Flickr explicitly indicate the trajectories of tourists. They can be employed to reveal the tourists’ preference on landmarks and routings of tourism. Most of existing works on routing searches are based on the trajectories of GPS-enabled devices’ users. From a distinct point of view, we attempt to propose a novel approach in which the basic unit of routing is separate road segment instead of GPS trajectory segment. In this paper, we build a recommendation system that provides users with the most popular landmarks as well as the best travel routings between the landmarks. By using Flickr geotaggged photos, the top ranking travel destinations in a city can be identified and then the best travel routes between the popular travel destinations are recommended. We apply a spatial clustering method to identify the main travel landmarks and subsequently rank these landmarks. Using machine learning method, we calculate the tourism popularity of the road in terms of relevant parameters, e.g., the number of users and the number of Point-of-Interests. These popularity assessments are integrated into the routing recommendation system. The routing recommendation system takes into consideration both the popularity assessment and the length of the road. The best route recommended to the user minimizes the distance while including maximal tourism popularity. Experiments were conducted in two different scenarios. The empirical results show that the recommendation system is able to provide the user good travel planning including both top ranking landmarks and suitable routings in a city. Besides, the system offers user-generated semantic information for the recommended routes. | ||
| 534 | |c 2013 | ||
| 650 | 4 | |a Routing planning | |
| 650 | 4 | |a Semantic routing | |
| 650 | 4 | |a Spatial data mining | |
| 650 | 4 | |a Travel recommendation | |
| 650 | 4 | |a Volunteered geographic information | |
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